gencalib(sampling)
gencalib()所属R语言包：sampling
g-weights of the generalized calibration estimator
克重量的广义校正估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Computes the g-weights of the generalized calibration estimator. The g-weights should lie in the specified bounds for the truncated and logit methods.
计算克重量的广义校正估计。 G-权重应该在于截断和logit方法在指定的范围内。
用法----------Usage----------
参数----------Arguments----------
参数：Xs
matrix of calibration variables.
矩阵的标定变量。
参数：Zs
matrix of instrumental variables with same dimension as Xs.
矩阵的工具变量X的尺寸相同。
参数：d
vector of initial weights.
的初始权重向量。
参数：total
vector of population totals.
向量的人口总数。
参数：q
vector of positive values accounting for heteroscedasticity; the variation of the g-weights is reduced for small values of q.
小Q值降低的正面价值会计的异方差性，变异的g权重向量。
参数：method
calibration method (linear, raking, logit, truncated).
校准方法（线性，耙，对数，截断）。
参数：bounds
vector of bounds for the g-weights used in the truncated and logit methods; 'low' is the smallest value and 'upp' is the largest value.
矢量界为G用权重的截断和logit的方法;“低”是最小值，UPP的是世界上最大的价值。
参数：description
if description=TRUE, summary of initial and final weights are printed, and their boxplots and histograms are drawn; by default, its value is FALSE.
如果描述= TRUE，总结的最初和最终的权重的印刷，箱线图和柱状图绘制，默认情况下，它的值是FALSE。
参数：max_iter
maximum number of iterations in the Newton's method.
牛顿法的迭代的最大数量。
参数：C
value of the centering constant, by default equals 1.
的中心不变，默认情况下的值等于1。
Details
详细信息----------Details----------
The generalized calibration or the instrument vector method computes the g-weights g_k=F(λ'z_k), where z_k is a vector with values defined for k\in s (or k\in r where r is the set of respondents) and sharing the dimension of the specified auxiliary vector x_k. The vectors z_k and x_k have to be stronlgy correlated. The vector λ is determined from the calibration equation ∑_{k\in s} d_kg_k x_k=∑_{k\in U} x_k or ∑_{k\in r} d_kg_k x_k=∑_{k\in U} x_k. The function F plays the same role as in the calibration method (see calib). If Xs=Zs the calibration method is obtain. If the method is "logit" the g-weights will be centered around the constant C, with low<C<upp. In the calibration method C=1 (see calib).
广义校准或仪器向量的方法计算权重的G g_k=F(λ'z_k),z_k是k\in s（k\in rr是一个向量，其定义的值组的受访者）和指定的辅助矢量x_k分享维度。的矢量z_k和x_k有，到被stronlgy相关。向量λ确定自校准方程∑_{k\in s} d_kg_k x_k=∑_{k\in U} x_k或∑_{k\in r} d_kg_k x_k=∑_{k\in U} x_k。 F的功能的校准方法（见calib）起着同样的作用。如果XS = ZS的校准方法获得。如果该方法是“罗吉特”克权重将围绕常数C，低<C <UPP。在校准法中C = 1（见calib）。
值----------Value----------
The function returns the vector of g-weights.
该函数返回G的权重向量。
参考文献----------References----------
Deville, J.-C. (2000). Generalized calibration and application for weighting for non-response, COMPSTAT 2000: proceedings in computational statistics, p. 65–76.<br> Estevao, V.M., and S盲rndal, C.E. (2000). A functional form approach to calibration. Journal of Official Statistics, 16, 379–399.<br> Kott, P.S. (2006). Using calibration weighting to adjust for nonresponse and coverage errors. Survey Methodology, 32, 133–142.<br>
参见----------See Also----------
checkcalibration, calib
checkcalibration，calib
实例----------Examples----------
############[＃＃＃＃＃＃＃＃＃＃＃]
## Example 1[＃示例1]
############[＃＃＃＃＃＃＃＃＃＃＃]
# matrix of sample calibration variables [基质样品的标定变量]
Xs=cbind(
c(1,1,1,1,1,0,0,0,0,0),
c(0,0,0,0,0,1,1,1,1,1),
c(1,2,3,4,5,6,7,8,9,10))
# inclusion probabilities[包含概率]
piks=rep(0.2,times=10)
# vector of population totals[向量的人口总数]
total=c(24,26,290)
# matrix of instrumental variables[工具变量矩阵]
Zs=Xs+matrix(runif(nrow(Xs)*ncol(Xs)),nrow(Xs),ncol(Xs))
# the g-weights using the truncated method[克的重量，使用截短的方法]
g=gencalib(Xs,Zs,d=1/piks,total,method="truncated",bounds=c(0.5,1.5))
# the calibration estimator of X is equal to the 'total' vector[X的估计是平等的“总”矢量校准]
t(g/piks)%*%Xs
# the g-weights are between lower and upper bounds[克重量的上界和下界之间的]
summary(g)
############[＃＃＃＃＃＃＃＃＃＃＃]
## Example 2[＃示例2]
############[＃＃＃＃＃＃＃＃＃＃＃]
# Example of generalized g-weights (linear, raking, truncated, logit),[广义g重量（线性，耙，截断，罗吉特）的例子，]
# with the data of Belgian municipalities as population.[比利时直辖市人口的数据。]
# Firstly, a sample is selected by means of Poisson sampling.[首先，将样品通过泊松抽样选择。]
# Secondly, the g-weights are calculated.[其次，对于g的权重计算。]
data(belgianmunicipalities)
attach(belgianmunicipalities)
# matrix of calibration variables for the population[人口校准变量矩阵]
X=cbind(Totaltaxation/mean(Totaltaxation),medianincome/mean(medianincome))
# selection of a sample with expected size equal to 200[选择与预期大小等于200的试样]
# by means of Poisson sampling[通过泊松抽样]
# the inclusion probabilities are proportional to the average income [包含概率是成正比的平均收入]
pik=inclusionprobabilities(averageincome,200)
N=length(pik) # population size[人口规模]
s=UPpoisson(pik) # sample[样品]
Xs=X[s==1,] # sample calibration variable matrix [样品校准变量矩阵]
piks=pik[s==1] # sample inclusion probabilities[样本包含概率]
n=length(piks) # sample size[样本量]
# vector of population totals of the calibration variables[校准变量矢量的人口总数]
total=c(t(rep(1,times=N))%*%X)
# the population total[人口总数]
total
Z=cbind(TaxableIncome/mean(TaxableIncome),averageincome/mean(averageincome))
# defines the instrumental variables[定义的工具变量]
Zs=Z[s==1,]
# computation of the generalized g-weights[的广义g权重计算]
# by means of different generalized calibration methods[由不同的广义的校准方法的装置]
g1=gencalib(Xs,Zs,d=1/piks,total,method="linear")
g2=gencalib(Xs,Zs,d=1/piks,total,method="raking")
g3=gencalib(Xs,Zs,d=1/piks,total,method="truncated",bounds=c(0.5,8))
g4=gencalib(Xs,Zs,d=1/piks,total,method="logit",bounds=c(0.5,1.5))
# In some cases, the calibration does not exist[在某些情况下，不存在校准]
# particularly when bounds are used.[特别是当使用界限。]
# if the calibration is possible, the calibration estimator of Xs is printed[如果校准是可能的，打印时，X的校准估计]
if(checkcalibration(Xs,d=1/piks,total,g1)$result) print(c((g1/piks)%*% Xs)) else print("error")
if(!is.null(g2))
if(checkcalibration(Xs,d=1/piks,total,g2)$result) print(c((g2/piks)%*% Xs)) else print("error")
if(!is.null(g3))
if(checkcalibration(Xs,d=1/piks,total,g3)$result) print(c((g3/piks)%*% Xs)) else print("error")
if(!is.null(g4))
if(checkcalibration(Xs,d=1/piks,total,g4)$result) print(c((g4/piks)%*% Xs)) else print("error")
############[＃＃＃＃＃＃＃＃＃＃＃]
## Example 3[＃示例3]
############[＃＃＃＃＃＃＃＃＃＃＃]
# Example of generalized calibration and adjustment for unit nonresponse in the 'calibration' vignette[在“校准”小插曲广义校准和调整的单元无应答的例子]
vignette("calibration", package="sampling")
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-9-29 21:47:57
